Transcriptomics: Lecture 2
Biotech 7005/Bioinf 3000
Frontiers of Biotechnology: Bioinformatics and Systems Modelling
The University of Adelaide
Frontiers of Biotechnology: Bioinformatics and Systems Modelling
The University of Adelaide
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Welcome To Country
I’d like to acknowledge the Kaurna people as the traditional owners and custodians of the land we know today as the Adelaide Plains, where I live & work.
I also acknowledge the deep feelings of attachment and relationship of the Kaurna people to their place.
I pay my respects to the cultural authority of Aboriginal and Torres Strait Islander peoples from other areas of Australia, and pay our respects to Elders past, present and emerging, and acknowledge any Aboriginal Australians who may be with us today
RNA-Seq
RNA Sequencing
According to Wang, Gerstein, and Snyder (2009)
RNA-Seq, also called RNA sequencing, is a particular technology-based sequencing technique which uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment, analyzing the continuously changing cellular transcriptome.
RNA Sequencing
- Microarrays are still published regularly
- Also used extensively for methylation
- RNA sequencing is now the dominant technology
- Strong improvement for:
- transcript-level resolution
- un-annotated genes
- allelic bias
- genomic variants
The RNA Population Of a Eukaryotic Cell
The Key Steps
- Focus from here on will be sequencing mRNA using short reads
- Library Preparation
- RNA Quality assessment
- Selecting target molecules
- Adding sequencing primers
- Sequencing
- Aligmnent + Quantitation
- DE Gene Detection
- Downstream Analysis
- (Optional) Nobel Prize
RNA Selection
- rRNA makes up about 80% of cellular RNA \(\rightarrow\) not of general interest
- tRNA ~15% + mRNA ~5% cellular RNA1
- Select for poly-adenylated RNA using oligo-dT-based methods
- Only extracts intact mRNA with a polyA tail (includes some ncRNA)
RNA Selection
- Enzymatically deplete rRNA sequences
- rRNA targeted using probes \(\implies\) dsRNA degraded
- Can additionally target hbRNA (whole blood)
Library Preparation
- RNA is then fragmented and size selected (200-300nt)
- Very short transcripts always lost during this step
- cDNA produced
- Sequencing adapters added
- Indexes are unique to each individual library \(\implies\) always have replicates
- Optionally contain Unique Molecular Identifiers (UMI)
\(\implies\) Helps identify PCR duplicates
- Most RNA-Seq now retains strand-of-origin information (Stranded RNA-Seq)
- During PCR only the first cDNA template retained
Library Preparation
Library Preparation
- Each double-stranded cDNA then has the final structure
Sequencing
Alignment and Quantitation
Genomic Alignment
- Alignment to a reference genome requires a splice-aware aligner
- A GTF (Gene Transfer File) required when building the index
\(\implies\) Provides all exon-transcript-gene co-ordinates - New Gencode, Ensembl etc releases at regular intervals
- A GTF (Gene Transfer File) required when building the index
- Most common aligners are STAR (Dobin et al. 2013) & hisat2 (Kim et al. 2019)
- Return alignments as a
bamfile
- Return alignments as a
- Aligned reads are then counted to provide gene-level counts
- htseq (Anders, Pyl, and Huber 2014) and featureCounts (Liao, Smyth, and Shi 2014) are very common
- The same GTF should be used as during indexing
Counting Alignments
- Some alignments align beautifully within exon structures
- Some overhang a little
- Unspliced mRNA?
- Some genes are overlapping
- Stranded libraries can resolve
- Maybe bacterial reads span genes within an operon
Gene-Level Counts
- The region encoding a gene is (relatively) well defined
- An alignment within a gene is easy to assign to that gene
- Much more difficult to identify which transcript it came from
- Many transcripts share multiple exons
- Splice Junctions were the earliest approach
Gene-Level Counts
Transcriptome Pseudo-Alignment
- An alternative is to provide a reference transcriptome
- Alignments no longer need to splice aware
- Reads can align to multiple transcripts
References
Anders, Simon, Paul Theodor Pyl, and Wolfgang Huber. 2014. “HTSeq—a Python Framework to Work with High-Throughput Sequencing Data.” Bioinformatics 31 (2): 166–69. https://doi.org/10.1093/bioinformatics/btu638.
Chan, Jia Jia, and Yvonne Tay. 2018. “Noncoding RNA:RNA Regulatory Networks in Cancer.” International Journal of Molecular Sciences 19 (5). https://doi.org/10.3390/ijms19051310.
Dobin, Alexander, Carrie A Davis, Felix Schlesinger, Jorg Drenkow, Chris Zaleski, Sonali Jha, Philippe Batut, Mark Chaisson, and Thomas R Gingeras. 2013. “STAR: Ultrafast Universal RNA-Seq Aligner.” Bioinformatics 29 (1): 15–21.
Kim, Daehwan, Joseph M Paggi, Chanhee Park, Christopher Bennett, and Steven L Salzberg. 2019. “Graph-Based Genome Alignment and Genotyping with HISAT2 and HISAT-Genotype.” Nat. Biotechnol. 37 (8): 907–15.
Liao, Yang, Gordon K Smyth, and Wei Shi. 2014. “featureCounts: An Efficient General Purpose Program for Assigning Sequence Reads to Genomic Features.” Bioinformatics 30 (7): 923–30.
Wang, Zhong, Mark Gerstein, and Michael Snyder. 2009. “RNA-Seq: A Revolutionary Tool for Transcriptomics.” Nat. Rev. Genet. 10 (1): 57–63.
Yu, Nancy Yiu-Lin, Björn M. Hallström, Linn Fagerberg, Fredrik Ponten, Hideya Kawaji, Piero Carninci, Alistair R. R. Forrest, et al. 2015. “Complementing Tissue Characterization by Integrating Transcriptome Profiling from the Human Protein Atlas and from the FANTOM5 Consortium.” Nucleic Acids Research 43 (14): 6787–98. https://doi.org/10.1093/nar/gkv608.
Footnotes
https://bionumbers.hms.harvard.edu/bionumber.aspx?s=n&v=5&id=100264↩︎